Every AI practitioner should know
This guide provides essential concepts for understanding, designing, and building better AI-powered applications. Each concept includes a simple definition and practical use case to help practitioners master LLMs effectively.
What is it? A model that generates text by predicting the most probable next token.
Use case: Chatbots, content generation, summarization.
What is it? A piece of text, such as a word or punctuation symbol.
Use case: Counting tokens to estimate cost or limits.
What is it? Process of converting text into a sequence of tokens.
Use case: Preparing text for model input.
What is it? Numerical vectors that represent the meaning of tokens.
Use case: Search, clustering, semantic similarity.
What is it? Mathematical space where embeddings get organized by meaning.
Use case: Visualizing relationships between concepts.
What is it? Internal variables that store models' learned patterns.
Use case: Model training and optimization.
What is it? Training on massive text data to learn general patterns.
Use case: Building a general-purpose language understanding model.
What is it? A pre-trained model that predicts text but doesn’t follow instructions.
Use case: Foundation for further fine-tuning.
What is it? Further trained to follow instructions and respond helpfully.
Use case: Chat assistants, Q&A, support bots.
What is it? Additional training on a smaller dataset to shape behavior.
Use case: Domain adaptation, custom tone or style.
What is it? Ensuring model behavior is helpful, honest, and harmless.
Use case: Reducing bias, ensuring safety.
What is it? Using human-ranked responses to guide model behavior.
Use case: Improving response quality and safety.
What is it? Complete input sent to model, including instructions and context.
Use case: Asking questions, giving tasks.
What is it? High-level instructions that define the model’s role and limits.
Use case: Setting behavior, tone, and boundaries.
What is it? Specific question or instruction provided by the user.
Use case: User queries, task requests.
What is it? Maximum number of tokens the model can process at once.
Use case: Managing long documents or chats.
What is it? Performing a task without examples in the input.
Use case: Simple Q&A, general knowledge tasks.
What is it? Including examples in prompt to guide output format.
Use case: Structured output, classification, style.
What is it? Prompting the model output through step-by-step reasoning.
Use case: Math problems, logic, complex tasks.
What is it? Process of generating output tokens from a trained model.
Use case: Real-time responses, content generation.
What is it? Time between sending a prompt and receiving output.
Use case: Optimizing user experience.
What is it? A parameter controlling randomness in token selection.
Use case: Lower = focus, Higher = creative.
What is it? Confident generation of incorrect or fabricated information.
Use case: Avoidable errors, reliable outputs.
What is it? Restricting outputs to provided or verifiable information.
Use case: Fact-checking, reliable outputs.
What is it? Retrieving external data and adding it to the prompt before generation.
Use case: Q&A over documents, knowledge bases.
What is it? A fixed, predefined sequence where the LLM follows set steps.
Use case: Automated pipelines, content moderation.
What is it? A system where LLM plans actions, then dynamically chooses steps and tools.
Use case: Autonomous assistants, tool-using agents.
What is it? Ability to process multiple input types, such as text and images.
Use case: Image understanding, vision-language tasks.
What is it? Standardized tests used to compare model capabilities.
Use case: Evaluating and comparing models.
What is it? Systems that block unsafe or inappropriate inputs and outputs.
Use case: Content safety, policy enforcement.
Imagine Sarah, a content creator, wants to generate engaging blog posts using an LLM. She starts by writing a User Prompt: "Write a blog post about the benefits of AI in healthcare." The LLM processes her input through Tokenization, converting her text into tokens. These tokens are mapped into Embeddings, which help the model understand the meaning and context of her request.
The model uses its Pre-trained Base Model and applies Fine-Tuning to ensure the output matches Sarah’s desired tone and style. As Sarah reviews the generated content, she notices a minor factual error. She uses a Grounding feature to verify the information and correct inaccuracies.
With the help of Guardrails, the system ensures the blog post avoids controversial or unsafe statements. Sarah publishes her article confidently, knowing the LLM has optimized her workflow and delivered reliable results.
Tip: Master these concepts to design effective prompts, build reliable LLM applications, and create safe, user-friendly AI experiences.